diff --git a/metagpt/tools/tool_convert.py b/metagpt/tools/tool_convert.py
index fc7cb9a15..fc29d0693 100644
--- a/metagpt/tools/tool_convert.py
+++ b/metagpt/tools/tool_convert.py
@@ -15,18 +15,19 @@ def convert_code_to_tool_schema(obj, include: list[str] = []):
             # method_doc = inspect.getdoc(method)
             method_doc = get_class_method_docstring(obj, name)
             if method_doc:
-                function_type = "function" if not inspect.iscoroutinefunction(method) else "async_function"
-                schema["methods"][name] = {"type": function_type, **docstring_to_schema(method_doc)}
+                schema["methods"][name] = function_docstring_to_schema(method, method_doc)
 
     elif inspect.isfunction(obj):
-        schema = {
-            "type": "function",
-            **docstring_to_schema(docstring),
-        }
+        schema = function_docstring_to_schema(obj, docstring)
 
     return schema
 
 
+def function_docstring_to_schema(fn_obj, docstring):
+    function_type = "function" if not inspect.iscoroutinefunction(fn_obj) else "async_function"
+    return {"type": function_type, **docstring_to_schema(docstring)}
+
+
 def docstring_to_schema(docstring: str):
     if docstring is None:
         return {}
diff --git a/tests/data/rsp_cache.json b/tests/data/rsp_cache.json
index c006dbafe..40d7d3953 100644
--- a/tests/data/rsp_cache.json
+++ b/tests/data/rsp_cache.json
@@ -335,16 +335,16 @@
             "RobustScale"
         ]
     },
-    "[{\"role\": \"user\", \"content\": \"\\n# Background\\nAs a data scientist, you need to help user to achieve their goal [构造数据集并进行数据清洗] step-by-step in an continuous Jupyter notebook.\\n\\n## Done Tasks\\n```python\\n                import pandas as pd\\n                df = pd.DataFrame({\\n                    'a': [1, 2, 3, 4, 5],\\n                    'b': [1.1, 2.2, 3.3, 4.4, np.nan],\\n                    'c': ['aa', 'bb', 'cc', 'dd', 'ee'],\\n                    'd': [1, 2, 3, 4, 5]\\n                })\\n```end\\n\\n## Current Task\\n对数据集进行数据清洗\\n\\n# Latest Data Info\\nLatest data info after previous tasks:\\n\\n\\n# Task\\nWrite complete code for 'Current Task'. And avoid duplicating code from 'Done Tasks', such as repeated import of packages, reading data, etc.\\nSpecifically, \\nThe current task is about data preprocessing, please note the following:\\n- Monitor data types per column, applying appropriate methods.\\n- Ensure operations are on existing dataset columns.\\n- Avoid writing processed data to files.\\n- Avoid any change to label column, such as standardization, etc.\\n- Prefer alternatives to one-hot encoding for categorical data.\\n- Only encode or scale necessary columns to allow for potential feature-specific engineering tasks (like time_extract, binning, extraction, etc.) later.\\n- Each step do data preprocessing to train, must do same for test separately at the same time.\\n\\n\\n# Capabilities\\n- You can utilize pre-defined tools in any code lines from 'Available Tools' in the form of Python Class.\\n- You can freely combine the use of any other public packages, like sklearn, numpy, pandas, etc..\\n\\n# Available Tools:\\nEach Class tool is described in JSON format. When you call a tool, import the tool from its path first.\\n{'FillMissingValue': {'type': 'class', 'description': 'Completing missing values with simple strategies.', 'methods': {'__init__': {'description': 'Initialize self.', 'parameters': {'properties': {'features': {'type': 'list', 'description': 'Columns to be processed.'}, 'strategy': {'type': 'str', 'description': \\\"The imputation strategy, notice 'mean' and 'median' can only be used for numeric features. Enum: ['mean', 'median', 'most_frequent', 'constant']. Defaults to 'mean'.\\\", 'default': \\\"'mean'\\\", 'enum': [\\\"'mean'\\\", \\\"'median'\\\", \\\"'most_frequent'\\\", \\\"'constant'\\\"]}, 'fill_value': {'type': 'int', 'description': 'Fill_value is used to replace all occurrences of missing_values. Defaults to None.', 'default': 'None'}}, 'required': ['features']}}, 'fit': {'description': 'Fit a model to be used in subsequent transform.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}}, 'fit_transform': {'description': 'Fit and transform the input DataFrame.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}, 'transform': {'description': 'Transform the input DataFrame with the fitted model.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}}, 'tool_path': 'metagpt/tools/libs/data_preprocess.py'}, 'MinMaxScale': {'type': 'class', 'description': 'Transform features by scaling each feature to a range, which is (0, 1).', 'methods': {'__init__': {'description': 'Initialize self.', 'parameters': {'properties': {'features': {'type': 'list', 'description': 'Columns to be processed.'}}, 'required': ['features']}}, 'fit': {'description': 'Fit a model to be used in subsequent transform.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}}, 'fit_transform': {'description': 'Fit and transform the input DataFrame.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}, 'transform': {'description': 'Transform the input DataFrame with the fitted model.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}}, 'tool_path': 'metagpt/tools/libs/data_preprocess.py'}, 'StandardScale': {'type': 'class', 'description': 'Standardize features by removing the mean and scaling to unit variance.', 'methods': {'__init__': {'description': 'Initialize self.', 'parameters': {'properties': {'features': {'type': 'list', 'description': 'Columns to be processed.'}}, 'required': ['features']}}, 'fit': {'description': 'Fit a model to be used in subsequent transform.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}}, 'fit_transform': {'description': 'Fit and transform the input DataFrame.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}, 'transform': {'description': 'Transform the input DataFrame with the fitted model.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}}, 'tool_path': 'metagpt/tools/libs/data_preprocess.py'}, 'MaxAbsScale': {'type': 'class', 'description': 'Scale each feature by its maximum absolute value.', 'methods': {'__init__': {'description': 'Initialize self.', 'parameters': {'properties': {'features': {'type': 'list', 'description': 'Columns to be processed.'}}, 'required': ['features']}}, 'fit': {'description': 'Fit a model to be used in subsequent transform.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}}, 'fit_transform': {'description': 'Fit and transform the input DataFrame.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}, 'transform': {'description': 'Transform the input DataFrame with the fitted model.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}}, 'tool_path': 'metagpt/tools/libs/data_preprocess.py'}, 'RobustScale': {'type': 'class', 'description': 'Apply the RobustScaler to scale features using statistics that are robust to outliers.', 'methods': {'__init__': {'description': 'Initialize self.', 'parameters': {'properties': {'features': {'type': 'list', 'description': 'Columns to be processed.'}}, 'required': ['features']}}, 'fit': {'description': 'Fit a model to be used in subsequent transform.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}}, 'fit_transform': {'description': 'Fit and transform the input DataFrame.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}, 'transform': {'description': 'Transform the input DataFrame with the fitted model.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}}, 'tool_path': 'metagpt/tools/libs/data_preprocess.py'}}\\n\\n# Output Example:\\nwhen current task is \\\"do data preprocess, like fill missing value, handle outliers, etc.\\\", the code can be like:\\n```python\\n# Step 1: fill missing value\\n# Tools used: ['FillMissingValue']\\nfrom metagpt.tools.libs.data_preprocess import FillMissingValue\\n\\ntrain_processed = train.copy()\\ntest_processed = test.copy()\\nnum_cols = train_processed.select_dtypes(include='number').columns.tolist()\\nif 'label' in num_cols:\\n    num_cols.remove('label')\\nfill_missing_value = FillMissingValue(features=num_cols, strategy='mean')\\nfill_missing_value.fit(train_processed)\\ntrain_processed = fill_missing_value.transform(train_processed)\\ntest_processed = fill_missing_value.transform(test_processed)\\n\\n# Step 2: handle outliers\\nfor col in num_cols:\\n    low, high = train_processed[col].quantile([0.01, 0.99])\\n    train_processed[col] = train_processed[col].clip(low, high)\\n    test_processed[col] = test_processed[col].clip(low, high)\\n```end\\n\\n# Constraints:\\n- Ensure the output new code is executable in the same Jupyter notebook with previous tasks code have been executed.\\n- Always prioritize using pre-defined tools for the same functionality.\\n- Always copy the DataFrame before processing it and use the copy to process.\\n\"}]": {
-        "code": "# Step 1: Fill missing values for numerical columns\nfrom metagpt.tools.libs.data_preprocess import FillMissingValue\n\n# Create a copy of the DataFrame to avoid modifying the original one\ndf_processed = df.copy()\n\n# Identify numerical columns with missing values\nnum_cols_with_missing = df_processed.select_dtypes(include='number').columns[df_processed.isnull().any()].tolist()\n\n# Initialize the FillMissingValue tool for numerical columns\nfill_missing_num = FillMissingValue(features=num_cols_with_missing, strategy='mean')\nfill_missing_num.fit(df_processed)\ndf_processed = fill_missing_num.transform(df_processed)\n\n# Step 2: Encode categorical columns\n# For simplicity, we will replace categorical values with a numerical code\n# This is a simple form of encoding that can be useful for tree-based models\n# and doesn't increase dimensionality like one-hot encoding\ncat_cols = df_processed.select_dtypes(include='object').columns.tolist()\nfor col in cat_cols:\n    df_processed[col] = df_processed[col].astype('category').cat.codes\n\n# Display the processed DataFrame\ndisplay(df_processed)"
+    "[{\"role\": \"user\", \"content\": \"\\n# Background\\nAs a data scientist, you need to help user to achieve their goal [构造数据集并进行数据清洗] step-by-step in an continuous Jupyter notebook.\\n\\n## Done Tasks\\n```python\\n                import pandas as pd\\n                df = pd.DataFrame({\\n                    'a': [1, 2, 3, 4, 5],\\n                    'b': [1.1, 2.2, 3.3, 4.4, np.nan],\\n                    'c': ['aa', 'bb', 'cc', 'dd', 'ee'],\\n                    'd': [1, 2, 3, 4, 5]\\n                })\\n```end\\n\\n## Current Task\\n对数据集进行数据清洗\\n\\n# Latest Data Info\\nLatest data info after previous tasks:\\n\\n\\n# Task\\nWrite complete code for 'Current Task'. And avoid duplicating code from 'Done Tasks', such as repeated import of packages, reading data, etc.\\nSpecifically, \\nThe current task is about data preprocessing, please note the following:\\n- Monitor data types per column, applying appropriate methods.\\n- Ensure operations are on existing dataset columns.\\n- Avoid writing processed data to files.\\n- Avoid any change to label column, such as standardization, etc.\\n- Prefer alternatives to one-hot encoding for categorical data.\\n- Only encode or scale necessary columns to allow for potential feature-specific engineering tasks (like time_extract, binning, extraction, etc.) later.\\n- Each step do data preprocessing to train, must do same for test separately at the same time.\\n\\n\\n# Capabilities\\n- You can utilize pre-defined tools in any code lines from 'Available Tools' in the form of Python Class.\\n- You can freely combine the use of any other public packages, like sklearn, numpy, pandas, etc..\\n\\n# Available Tools:\\nEach Class tool is described in JSON format. When you call a tool, import the tool from its path first.\\n{'FillMissingValue': {'type': 'class', 'description': 'Completing missing values with simple strategies.', 'methods': {'__init__': {'type': 'function', 'description': 'Initialize self.', 'parameters': {'properties': {'features': {'type': 'list', 'description': 'Columns to be processed.'}, 'strategy': {'type': 'str', 'description': \\\"The imputation strategy, notice 'mean' and 'median' can only be used for numeric features. Enum: ['mean', 'median', 'most_frequent', 'constant']. Defaults to 'mean'.\\\", 'default': \\\"'mean'\\\", 'enum': [\\\"'mean'\\\", \\\"'median'\\\", \\\"'most_frequent'\\\", \\\"'constant'\\\"]}, 'fill_value': {'type': 'int', 'description': 'Fill_value is used to replace all occurrences of missing_values. Defaults to None.', 'default': 'None'}}, 'required': ['features']}}, 'fit': {'type': 'function', 'description': 'Fit a model to be used in subsequent transform.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}}, 'fit_transform': {'type': 'function', 'description': 'Fit and transform the input DataFrame.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}, 'transform': {'type': 'function', 'description': 'Transform the input DataFrame with the fitted model.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}}, 'tool_path': 'metagpt/tools/libs/data_preprocess.py'}, 'MinMaxScale': {'type': 'class', 'description': 'Transform features by scaling each feature to a range, which is (0, 1).', 'methods': {'__init__': {'type': 'function', 'description': 'Initialize self.', 'parameters': {'properties': {'features': {'type': 'list', 'description': 'Columns to be processed.'}}, 'required': ['features']}}, 'fit': {'type': 'function', 'description': 'Fit a model to be used in subsequent transform.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}}, 'fit_transform': {'type': 'function', 'description': 'Fit and transform the input DataFrame.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}, 'transform': {'type': 'function', 'description': 'Transform the input DataFrame with the fitted model.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}}, 'tool_path': 'metagpt/tools/libs/data_preprocess.py'}, 'StandardScale': {'type': 'class', 'description': 'Standardize features by removing the mean and scaling to unit variance.', 'methods': {'__init__': {'type': 'function', 'description': 'Initialize self.', 'parameters': {'properties': {'features': {'type': 'list', 'description': 'Columns to be processed.'}}, 'required': ['features']}}, 'fit': {'type': 'function', 'description': 'Fit a model to be used in subsequent transform.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}}, 'fit_transform': {'type': 'function', 'description': 'Fit and transform the input DataFrame.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}, 'transform': {'type': 'function', 'description': 'Transform the input DataFrame with the fitted model.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}}, 'tool_path': 'metagpt/tools/libs/data_preprocess.py'}, 'MaxAbsScale': {'type': 'class', 'description': 'Scale each feature by its maximum absolute value.', 'methods': {'__init__': {'type': 'function', 'description': 'Initialize self.', 'parameters': {'properties': {'features': {'type': 'list', 'description': 'Columns to be processed.'}}, 'required': ['features']}}, 'fit': {'type': 'function', 'description': 'Fit a model to be used in subsequent transform.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}}, 'fit_transform': {'type': 'function', 'description': 'Fit and transform the input DataFrame.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}, 'transform': {'type': 'function', 'description': 'Transform the input DataFrame with the fitted model.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}}, 'tool_path': 'metagpt/tools/libs/data_preprocess.py'}, 'RobustScale': {'type': 'class', 'description': 'Apply the RobustScaler to scale features using statistics that are robust to outliers.', 'methods': {'__init__': {'type': 'function', 'description': 'Initialize self.', 'parameters': {'properties': {'features': {'type': 'list', 'description': 'Columns to be processed.'}}, 'required': ['features']}}, 'fit': {'type': 'function', 'description': 'Fit a model to be used in subsequent transform.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}}, 'fit_transform': {'type': 'function', 'description': 'Fit and transform the input DataFrame.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}, 'transform': {'type': 'function', 'description': 'Transform the input DataFrame with the fitted model.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}}, 'tool_path': 'metagpt/tools/libs/data_preprocess.py'}}\\n\\n# Output Example:\\nwhen current task is \\\"do data preprocess, like fill missing value, handle outliers, etc.\\\", the code can be like:\\n```python\\n# Step 1: fill missing value\\n# Tools used: ['FillMissingValue']\\nfrom metagpt.tools.libs.data_preprocess import FillMissingValue\\n\\ntrain_processed = train.copy()\\ntest_processed = test.copy()\\nnum_cols = train_processed.select_dtypes(include='number').columns.tolist()\\nif 'label' in num_cols:\\n    num_cols.remove('label')\\nfill_missing_value = FillMissingValue(features=num_cols, strategy='mean')\\nfill_missing_value.fit(train_processed)\\ntrain_processed = fill_missing_value.transform(train_processed)\\ntest_processed = fill_missing_value.transform(test_processed)\\n\\n# Step 2: handle outliers\\nfor col in num_cols:\\n    low, high = train_processed[col].quantile([0.01, 0.99])\\n    train_processed[col] = train_processed[col].clip(low, high)\\n    test_processed[col] = test_processed[col].clip(low, high)\\n```end\\n\\n# Constraints:\\n- Ensure the output new code is executable in the same Jupyter notebook with previous tasks code have been executed.\\n- Always prioritize using pre-defined tools for the same functionality.\\n- Always copy the DataFrame before processing it and use the copy to process.\\n\"}]": {
+        "code": "# Step 1: Fill missing values\nfrom metagpt.tools.libs.data_preprocess import FillMissingValue\n\n# Create a copy of the DataFrame to avoid modifying the original one\ndf_processed = df.copy()\n\n# Define the columns with missing values\nmissing_value_columns = df_processed.columns[df_processed.isnull().any()].tolist()\n\n# Initialize the FillMissingValue tool with the strategy to fill missing values with the mean\nfill_missing_value = FillMissingValue(features=missing_value_columns, strategy='mean')\n\n# Fit and transform the DataFrame\ndf_processed = fill_missing_value.fit_transform(df_processed)\n\n# Step 2: Scale numeric columns\nfrom metagpt.tools.libs.data_preprocess import MinMaxScale\n\n# Define the numeric columns to be scaled\nnumeric_columns = df_processed.select_dtypes(include=['int64', 'float64']).columns.tolist()\n\n# Initialize the MinMaxScale tool\nminmax_scale = MinMaxScale(features=numeric_columns)\n\n# Fit and transform the DataFrame\ndf_processed = minmax_scale.fit_transform(df_processed)\n\n# Display the processed DataFrame\ndisplay(df_processed)"
     },
     "[{\"role\": \"user\", \"content\": \"\\n## User Requirement:\\nclean and preprocess the data\\n\\n## Task\\nRecommend up to five tools from 'Available Tools' that can help solve the 'User Requirement'. \\n\\n## Available Tools:\\n{'FillMissingValue': 'Filling missing values', 'SplitBins': 'Bin continuous data into intervals and return the bin identifier encoded as an integer value'}\\n\\n## Tool Selection and Instructions:\\n- Select tools most relevant to completing the 'User Requirement'.\\n- If you believe that no tools are suitable, indicate with an empty list.\\n- Only list the names of the tools, not the full schema of each tool.\\n- Ensure selected tools are listed in 'Available Tools'.\\n\"}]": {
         "recommend_tools": [
             "FillMissingValue"
         ]
     },
-    "[{\"role\": \"system\", \"content\": \"You are Code Interpreter, a world-class programmer that can complete any goal by executing code. Strictly follow the plan and generate code step by step. Each step of the code will be executed on the user's machine, and the user will provide the code execution results to you.**Notice: The code for the next step depends on the code for the previous step. Must reuse variables in the lastest other code directly, dont creat it again, it is very import for you. Use !pip install in a standalone block to install missing packages.Usually the libraries you need are already installed.Dont check if packages already imported.**\"}, {\"role\": \"user\", \"content\": \"\\n## User Requirement\\n构造数据集并进行数据清洗\\n## Context\\n\\n## Current Plan\\n[Task(task_id='1', dependent_task_ids=[], instruction='随机生成一个pandas DataFrame数据集', task_type='other', code=\\\"\\\\n                import pandas as pd\\\\n                df = pd.DataFrame({\\\\n                    'a': [1, 2, 3, 4, 5],\\\\n                    'b': [1.1, 2.2, 3.3, 4.4, np.nan],\\\\n                    'c': ['aa', 'bb', 'cc', 'dd', 'ee'],\\\\n                    'd': [1, 2, 3, 4, 5]\\\\n                })\\\\n                \\\", result='', is_success=False, is_finished=True), Task(task_id='2', dependent_task_ids=['1'], instruction='对数据集进行数据清洗', task_type='data_preprocess', code='', result='', is_success=False, is_finished=False)]\\n## Current Task\\n{\\\"task_id\\\":\\\"2\\\",\\\"dependent_task_ids\\\":[\\\"1\\\"],\\\"instruction\\\":\\\"对数据集进行数据清洗\\\",\\\"task_type\\\":\\\"data_preprocess\\\",\\\"code\\\":\\\"\\\",\\\"result\\\":\\\"\\\",\\\"is_success\\\":false,\\\"is_finished\\\":false}\\n\"}, {\"role\": \"user\", \"content\": \"\\n# Instruction\\nWrite complete code for 'Current Task'. And avoid duplicating code from finished tasks, such as repeated import of packages, reading data, etc.\\nSpecifically, \\nThe current task is about data preprocessing, please note the following:\\n- Monitor data types per column, applying appropriate methods.\\n- Ensure operations are on existing dataset columns.\\n- Avoid writing processed data to files.\\n- Avoid any change to label column, such as standardization, etc.\\n- Prefer alternatives to one-hot encoding for categorical data.\\n- Only encode or scale necessary columns to allow for potential feature-specific engineering tasks (like time_extract, binning, extraction, etc.) later.\\n- Each step do data preprocessing to train, must do same for test separately at the same time.\\n\\n\\n# Capabilities\\n- You can utilize pre-defined tools in any code lines from 'Available Tools' in the form of Python Class.\\n- You can freely combine the use of any other public packages, like sklearn, numpy, pandas, etc..\\n\\n# Available Tools (can be empty):\\nEach Class tool is described in JSON format. When you call a tool, import the tool first.\\n{'FillMissingValue': {'type': 'class', 'description': 'Completing missing values with simple strategies.', 'methods': {'__init__': {'description': 'Initialize self.', 'parameters': {'properties': {'features': {'type': 'list', 'description': 'Columns to be processed.'}, 'strategy': {'type': 'str', 'description': \\\"The imputation strategy, notice 'mean' and 'median' can only be used for numeric features. Enum: ['mean', 'median', 'most_frequent', 'constant']. Defaults to 'mean'.\\\", 'default': \\\"'mean'\\\", 'enum': [\\\"'mean'\\\", \\\"'median'\\\", \\\"'most_frequent'\\\", \\\"'constant'\\\"]}, 'fill_value': {'type': 'int', 'description': 'Fill_value is used to replace all occurrences of missing_values. Defaults to None.', 'default': 'None'}}, 'required': ['features']}}, 'fit': {'description': 'Fit a model to be used in subsequent transform.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}}, 'fit_transform': {'description': 'Fit and transform the input DataFrame.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}, 'transform': {'description': 'Transform the input DataFrame with the fitted model.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}}, 'tool_path': 'metagpt/tools/libs/data_preprocess.py'}, 'MinMaxScale': {'type': 'class', 'description': 'Transform features by scaling each feature to a range, which is (0, 1).', 'methods': {'__init__': {'description': 'Initialize self.', 'parameters': {'properties': {'features': {'type': 'list', 'description': 'Columns to be processed.'}}, 'required': ['features']}}, 'fit': {'description': 'Fit a model to be used in subsequent transform.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}}, 'fit_transform': {'description': 'Fit and transform the input DataFrame.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}, 'transform': {'description': 'Transform the input DataFrame with the fitted model.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}}, 'tool_path': 'metagpt/tools/libs/data_preprocess.py'}, 'StandardScale': {'type': 'class', 'description': 'Standardize features by removing the mean and scaling to unit variance.', 'methods': {'__init__': {'description': 'Initialize self.', 'parameters': {'properties': {'features': {'type': 'list', 'description': 'Columns to be processed.'}}, 'required': ['features']}}, 'fit': {'description': 'Fit a model to be used in subsequent transform.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}}, 'fit_transform': {'description': 'Fit and transform the input DataFrame.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}, 'transform': {'description': 'Transform the input DataFrame with the fitted model.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}}, 'tool_path': 'metagpt/tools/libs/data_preprocess.py'}, 'MaxAbsScale': {'type': 'class', 'description': 'Scale each feature by its maximum absolute value.', 'methods': {'__init__': {'description': 'Initialize self.', 'parameters': {'properties': {'features': {'type': 'list', 'description': 'Columns to be processed.'}}, 'required': ['features']}}, 'fit': {'description': 'Fit a model to be used in subsequent transform.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}}, 'fit_transform': {'description': 'Fit and transform the input DataFrame.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}, 'transform': {'description': 'Transform the input DataFrame with the fitted model.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}}, 'tool_path': 'metagpt/tools/libs/data_preprocess.py'}, 'RobustScale': {'type': 'class', 'description': 'Apply the RobustScaler to scale features using statistics that are robust to outliers.', 'methods': {'__init__': {'description': 'Initialize self.', 'parameters': {'properties': {'features': {'type': 'list', 'description': 'Columns to be processed.'}}, 'required': ['features']}}, 'fit': {'description': 'Fit a model to be used in subsequent transform.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}}, 'fit_transform': {'description': 'Fit and transform the input DataFrame.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}, 'transform': {'description': 'Transform the input DataFrame with the fitted model.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}}, 'tool_path': 'metagpt/tools/libs/data_preprocess.py'}}\\n\\n# Constraints:\\n- Ensure the output new code is executable in the same Jupyter notebook with previous tasks code have been executed.\\n- Always prioritize using pre-defined tools for the same functionality.\\n\"}]": {
-        "code": "from metagpt.tools.libs.data_preprocess import FillMissingValue\n\n# Instantiate the FillMissingValue class for column 'b'\nfill_missing_b = FillMissingValue(features=['b'], strategy='mean')\n\n# Fit and transform the DataFrame\ndf = fill_missing_b.fit_transform(df)"
+    "[{\"role\": \"system\", \"content\": \"You are Code Interpreter, a world-class programmer that can complete any goal by executing code. Strictly follow the plan and generate code step by step. Each step of the code will be executed on the user's machine, and the user will provide the code execution results to you.**Notice: The code for the next step depends on the code for the previous step. Must reuse variables in the lastest other code directly, dont creat it again, it is very import for you. Use !pip install in a standalone block to install missing packages.Usually the libraries you need are already installed.Dont check if packages already imported.**\"}, {\"role\": \"user\", \"content\": \"\\n## User Requirement\\n构造数据集并进行数据清洗\\n## Context\\n\\n## Current Plan\\n[Task(task_id='1', dependent_task_ids=[], instruction='随机生成一个pandas DataFrame数据集', task_type='other', code=\\\"\\\\n                import pandas as pd\\\\n                df = pd.DataFrame({\\\\n                    'a': [1, 2, 3, 4, 5],\\\\n                    'b': [1.1, 2.2, 3.3, 4.4, np.nan],\\\\n                    'c': ['aa', 'bb', 'cc', 'dd', 'ee'],\\\\n                    'd': [1, 2, 3, 4, 5]\\\\n                })\\\\n                \\\", result='', is_success=False, is_finished=True), Task(task_id='2', dependent_task_ids=['1'], instruction='对数据集进行数据清洗', task_type='data_preprocess', code='', result='', is_success=False, is_finished=False)]\\n## Current Task\\n{\\\"task_id\\\":\\\"2\\\",\\\"dependent_task_ids\\\":[\\\"1\\\"],\\\"instruction\\\":\\\"对数据集进行数据清洗\\\",\\\"task_type\\\":\\\"data_preprocess\\\",\\\"code\\\":\\\"\\\",\\\"result\\\":\\\"\\\",\\\"is_success\\\":false,\\\"is_finished\\\":false}\\n\"}, {\"role\": \"user\", \"content\": \"\\n# Instruction\\nWrite complete code for 'Current Task'. And avoid duplicating code from finished tasks, such as repeated import of packages, reading data, etc.\\nSpecifically, \\nThe current task is about data preprocessing, please note the following:\\n- Monitor data types per column, applying appropriate methods.\\n- Ensure operations are on existing dataset columns.\\n- Avoid writing processed data to files.\\n- Avoid any change to label column, such as standardization, etc.\\n- Prefer alternatives to one-hot encoding for categorical data.\\n- Only encode or scale necessary columns to allow for potential feature-specific engineering tasks (like time_extract, binning, extraction, etc.) later.\\n- Each step do data preprocessing to train, must do same for test separately at the same time.\\n\\n\\n# Capabilities\\n- You can utilize pre-defined tools in any code lines from 'Available Tools' in the form of Python Class.\\n- You can freely combine the use of any other public packages, like sklearn, numpy, pandas, etc..\\n\\n# Available Tools (can be empty):\\nEach Class tool is described in JSON format. When you call a tool, import the tool first.\\n{'FillMissingValue': {'type': 'class', 'description': 'Completing missing values with simple strategies.', 'methods': {'__init__': {'type': 'function', 'description': 'Initialize self.', 'parameters': {'properties': {'features': {'type': 'list', 'description': 'Columns to be processed.'}, 'strategy': {'type': 'str', 'description': \\\"The imputation strategy, notice 'mean' and 'median' can only be used for numeric features. Enum: ['mean', 'median', 'most_frequent', 'constant']. Defaults to 'mean'.\\\", 'default': \\\"'mean'\\\", 'enum': [\\\"'mean'\\\", \\\"'median'\\\", \\\"'most_frequent'\\\", \\\"'constant'\\\"]}, 'fill_value': {'type': 'int', 'description': 'Fill_value is used to replace all occurrences of missing_values. Defaults to None.', 'default': 'None'}}, 'required': ['features']}}, 'fit': {'type': 'function', 'description': 'Fit a model to be used in subsequent transform.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}}, 'fit_transform': {'type': 'function', 'description': 'Fit and transform the input DataFrame.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}, 'transform': {'type': 'function', 'description': 'Transform the input DataFrame with the fitted model.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}}, 'tool_path': 'metagpt/tools/libs/data_preprocess.py'}, 'MinMaxScale': {'type': 'class', 'description': 'Transform features by scaling each feature to a range, which is (0, 1).', 'methods': {'__init__': {'type': 'function', 'description': 'Initialize self.', 'parameters': {'properties': {'features': {'type': 'list', 'description': 'Columns to be processed.'}}, 'required': ['features']}}, 'fit': {'type': 'function', 'description': 'Fit a model to be used in subsequent transform.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}}, 'fit_transform': {'type': 'function', 'description': 'Fit and transform the input DataFrame.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}, 'transform': {'type': 'function', 'description': 'Transform the input DataFrame with the fitted model.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}}, 'tool_path': 'metagpt/tools/libs/data_preprocess.py'}, 'StandardScale': {'type': 'class', 'description': 'Standardize features by removing the mean and scaling to unit variance.', 'methods': {'__init__': {'type': 'function', 'description': 'Initialize self.', 'parameters': {'properties': {'features': {'type': 'list', 'description': 'Columns to be processed.'}}, 'required': ['features']}}, 'fit': {'type': 'function', 'description': 'Fit a model to be used in subsequent transform.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}}, 'fit_transform': {'type': 'function', 'description': 'Fit and transform the input DataFrame.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}, 'transform': {'type': 'function', 'description': 'Transform the input DataFrame with the fitted model.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}}, 'tool_path': 'metagpt/tools/libs/data_preprocess.py'}, 'MaxAbsScale': {'type': 'class', 'description': 'Scale each feature by its maximum absolute value.', 'methods': {'__init__': {'type': 'function', 'description': 'Initialize self.', 'parameters': {'properties': {'features': {'type': 'list', 'description': 'Columns to be processed.'}}, 'required': ['features']}}, 'fit': {'type': 'function', 'description': 'Fit a model to be used in subsequent transform.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}}, 'fit_transform': {'type': 'function', 'description': 'Fit and transform the input DataFrame.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}, 'transform': {'type': 'function', 'description': 'Transform the input DataFrame with the fitted model.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}}, 'tool_path': 'metagpt/tools/libs/data_preprocess.py'}, 'RobustScale': {'type': 'class', 'description': 'Apply the RobustScaler to scale features using statistics that are robust to outliers.', 'methods': {'__init__': {'type': 'function', 'description': 'Initialize self.', 'parameters': {'properties': {'features': {'type': 'list', 'description': 'Columns to be processed.'}}, 'required': ['features']}}, 'fit': {'type': 'function', 'description': 'Fit a model to be used in subsequent transform.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}}, 'fit_transform': {'type': 'function', 'description': 'Fit and transform the input DataFrame.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}, 'transform': {'type': 'function', 'description': 'Transform the input DataFrame with the fitted model.', 'parameters': {'properties': {'df': {'type': 'pd.DataFrame', 'description': 'The input DataFrame.'}}, 'required': ['df']}, 'returns': [{'type': 'pd.DataFrame', 'description': 'The transformed DataFrame.'}]}}, 'tool_path': 'metagpt/tools/libs/data_preprocess.py'}}\\n\\n# Constraints:\\n- Ensure the output new code is executable in the same Jupyter notebook with previous tasks code have been executed.\\n- Always prioritize using pre-defined tools for the same functionality.\\n\"}]": {
+        "code": "from metagpt.tools.libs.data_preprocess import FillMissingValue\n\n# Instantiate the FillMissingValue class for column 'b'\nfill_missing_b = FillMissingValue(features=['b'], strategy='mean')\n\n# Fit and transform the DataFrame\n# Since we only have one DataFrame, we will use it as both train and test for demonstration\n# In practice, train and test data should be handled separately\ntrain_df = fill_missing_b.fit_transform(df)\ntest_df = fill_missing_b.transform(df)\n\n# Check the results\ntrain_df\n"
     },
     "[{\"role\": \"system\", \"content\": \"You are Code Interpreter, a world-class programmer that can complete any goal by executing code. Strictly follow the plan and generate code step by step. Each step of the code will be executed on the user's machine, and the user will provide the code execution results to you.**Notice: The code for the next step depends on the code for the previous step. Must reuse variables in the lastest other code directly, dont creat it again, it is very import for you. Use !pip install in a standalone block to install missing packages.Usually the libraries you need are already installed.Dont check if packages already imported.**\"}, {\"role\": \"user\", \"content\": \"\\n    ## User Requirement\\n    read a dataset test.csv and print its head\\n    ## Current Plan\\n    [\\n        {\\n            \\\"task_id\\\": \\\"1\\\",\\n            \\\"dependent_task_ids\\\": [],\\n            \\\"instruction\\\": \\\"import pandas and load the dataset from 'test.csv'.\\\",\\n            \\\"task_type\\\": \\\"\\\",\\n            \\\"code\\\": \\\"\\\",\\n            \\\"result\\\": \\\"\\\",\\n            \\\"is_finished\\\": false\\n        },\\n        {\\n            \\\"task_id\\\": \\\"2\\\",\\n            \\\"dependent_task_ids\\\": [\\n                \\\"1\\\"\\n            ],\\n            \\\"instruction\\\": \\\"Print the head of the dataset to display the first few rows.\\\",\\n            \\\"task_type\\\": \\\"\\\",\\n            \\\"code\\\": \\\"\\\",\\n            \\\"result\\\": \\\"\\\",\\n            \\\"is_finished\\\": false\\n        }\\n    ]\\n    ## Current Task\\n    {\\\"task_id\\\": \\\"1\\\", \\\"dependent_task_ids\\\": [], \\\"instruction\\\": \\\"import pandas and load the dataset from 'test.csv'.\\\", \\\"task_type\\\": \\\"\\\", \\\"code\\\": \\\"\\\", \\\"result\\\": \\\"\\\", \\\"is_finished\\\": false}\\n    \"}, {\"role\": \"assistant\", \"content\": \"import pandas as pd\\ndata = pd.read_excel('test.csv')\\ndata\"}, {\"role\": \"user\", \"content\": \"\\n    Traceback (most recent call last):\\n        File \\\"<stdin>\\\", line 2, in <module>\\n        File \\\"/Users/gary/miniconda3/envs/py39_scratch/lib/python3.9/site-packages/pandas/io/excel/_base.py\\\", line 478, in read_excel\\n            io = ExcelFile(io, storage_options=storage_options, engine=engine)\\n        File \\\"/Users/gary/miniconda3/envs/py39_scratch/lib/python3.9/site-packages/pandas/io/excel/_base.py\\\", line 1500, in __init__\\n            raise ValueError(\\n        ValueError: Excel file format cannot be determined, you must specify an engine manually.\\n    \"}]": {
         "language": "python",
@@ -376,11 +376,11 @@
     "[{\"role\": \"system\", \"content\": \"You are Code Interpreter, a world-class programmer that can complete any goal by executing code. Strictly follow the plan and generate code step by step. Each step of the code will be executed on the user's machine, and the user will provide the code execution results to you.**Notice: The code for the next step depends on the code for the previous step. Must reuse variables in the lastest other code directly, dont creat it again, it is very import for you. Use !pip install in a standalone block to install missing packages.Usually the libraries you need are already installed.Dont check if packages already imported.**\"}, {\"role\": \"user\", \"content\": \"\\n## User Requirement\\nRun data analysis on sklearn Iris dataset, include a plot\\n## Context\\n\\n## Current Plan\\n[\\n    {\\n        \\\"task_id\\\": \\\"1\\\",\\n        \\\"dependent_task_ids\\\": [],\\n        \\\"instruction\\\": \\\"Load the sklearn Iris dataset.\\\",\\n        \\\"task_type\\\": \\\"other\\\",\\n        \\\"code\\\": \\\"\\\",\\n        \\\"result\\\": \\\"\\\",\\n        \\\"is_success\\\": false,\\n        \\\"is_finished\\\": false\\n    },\\n    {\\n        \\\"task_id\\\": \\\"2\\\",\\n        \\\"dependent_task_ids\\\": [\\n            \\\"1\\\"\\n        ],\\n        \\\"instruction\\\": \\\"Perform exploratory data analysis on the Iris dataset.\\\",\\n        \\\"task_type\\\": \\\"eda\\\",\\n        \\\"code\\\": \\\"\\\",\\n        \\\"result\\\": \\\"\\\",\\n        \\\"is_success\\\": false,\\n        \\\"is_finished\\\": false\\n    },\\n    {\\n        \\\"task_id\\\": \\\"3\\\",\\n        \\\"dependent_task_ids\\\": [\\n            \\\"2\\\"\\n        ],\\n        \\\"instruction\\\": \\\"Create a plot visualizing the Iris dataset.\\\",\\n        \\\"task_type\\\": \\\"other\\\",\\n        \\\"code\\\": \\\"\\\",\\n        \\\"result\\\": \\\"\\\",\\n        \\\"is_success\\\": false,\\n        \\\"is_finished\\\": false\\n    }\\n]\\n## Current Task\\n{\\\"task_id\\\":\\\"1\\\",\\\"dependent_task_ids\\\":[],\\\"instruction\\\":\\\"Load the sklearn Iris dataset.\\\",\\\"task_type\\\":\\\"other\\\",\\\"code\\\":\\\"\\\",\\\"result\\\":\\\"\\\",\\\"is_success\\\":false,\\\"is_finished\\\":false}\\n\"}, {\"role\": \"user\", \"content\": \"\\n# Instruction\\nWrite complete code for 'Current Task'. And avoid duplicating code from finished tasks, such as repeated import of packages, reading data, etc.\\nSpecifically, \\n\\n# Capabilities\\n- You can utilize pre-defined tools in any code lines from 'Available Tools' in the form of Python Class.\\n- You can freely combine the use of any other public packages, like sklearn, numpy, pandas, etc..\\n\\n# Available Tools (can be empty):\\nEach Class tool is described in JSON format. When you call a tool, import the tool first.\\n{}\\n\\n# Constraints:\\n- Ensure the output new code is executable in the same Jupyter notebook with previous tasks code have been executed.\\n- Always prioritize using pre-defined tools for the same functionality.\\n\"}]": {
         "code": "from sklearn.datasets import load_iris\niris_data = load_iris()"
     },
-    "[{\"role\": \"system\", \"content\": \"You are Code Interpreter, a world-class programmer that can complete any goal by executing code. Strictly follow the plan and generate code step by step. Each step of the code will be executed on the user's machine, and the user will provide the code execution results to you.**Notice: The code for the next step depends on the code for the previous step. Must reuse variables in the lastest other code directly, dont creat it again, it is very import for you. Use !pip install in a standalone block to install missing packages.Usually the libraries you need are already installed.Dont check if packages already imported.**\"}, {\"role\": \"user\", \"content\": \"\\n## User Requirement\\nRun data analysis on sklearn Iris dataset, include a plot\\n## Context\\n\\n## Current Plan\\n[\\n    {\\n        \\\"task_id\\\": \\\"1\\\",\\n        \\\"dependent_task_ids\\\": [],\\n        \\\"instruction\\\": \\\"Load the sklearn Iris dataset.\\\",\\n        \\\"task_type\\\": \\\"other\\\",\\n        \\\"code\\\": \\\"from sklearn.datasets import load_iris\\\\niris_data = load_iris()\\\",\\n        \\\"result\\\": \\\"a successful run\\\",\\n        \\\"is_success\\\": true,\\n        \\\"is_finished\\\": true\\n    },\\n    {\\n        \\\"task_id\\\": \\\"2\\\",\\n        \\\"dependent_task_ids\\\": [\\n            \\\"1\\\"\\n        ],\\n        \\\"instruction\\\": \\\"Perform exploratory data analysis on the Iris dataset.\\\",\\n        \\\"task_type\\\": \\\"eda\\\",\\n        \\\"code\\\": \\\"\\\",\\n        \\\"result\\\": \\\"\\\",\\n        \\\"is_success\\\": false,\\n        \\\"is_finished\\\": false\\n    },\\n    {\\n        \\\"task_id\\\": \\\"3\\\",\\n        \\\"dependent_task_ids\\\": [\\n            \\\"2\\\"\\n        ],\\n        \\\"instruction\\\": \\\"Create a plot visualizing the Iris dataset.\\\",\\n        \\\"task_type\\\": \\\"other\\\",\\n        \\\"code\\\": \\\"\\\",\\n        \\\"result\\\": \\\"\\\",\\n        \\\"is_success\\\": false,\\n        \\\"is_finished\\\": false\\n    }\\n]\\n## Current Task\\n{\\\"task_id\\\":\\\"2\\\",\\\"dependent_task_ids\\\":[\\\"1\\\"],\\\"instruction\\\":\\\"Perform exploratory data analysis on the Iris dataset.\\\",\\\"task_type\\\":\\\"eda\\\",\\\"code\\\":\\\"\\\",\\\"result\\\":\\\"\\\",\\\"is_success\\\":false,\\\"is_finished\\\":false}\\n\"}, {\"role\": \"user\", \"content\": \"\\n# Instruction\\nWrite complete code for 'Current Task'. And avoid duplicating code from finished tasks, such as repeated import of packages, reading data, etc.\\nSpecifically, \\n\\n# Capabilities\\n- You can utilize pre-defined tools in any code lines from 'Available Tools' in the form of Python Class.\\n- You can freely combine the use of any other public packages, like sklearn, numpy, pandas, etc..\\n\\n# Available Tools (can be empty):\\nEach Class tool is described in JSON format. When you call a tool, import the tool first.\\n{}\\n\\n# Constraints:\\n- Ensure the output new code is executable in the same Jupyter notebook with previous tasks code have been executed.\\n- Always prioritize using pre-defined tools for the same functionality.\\n\"}]": {
-        "code": "import pandas as pd\nimport numpy as np\n\n# Convert the loaded Iris dataset to a DataFrame for easier manipulation\niris_df = pd.DataFrame(data=np.c_[iris_data['data'], iris_data['target']], columns=iris_data['feature_names'] + ['target'])\n\n# Show basic information about the dataset\ndataset_info = iris_df.info()\n\n# Show basic statistics of the dataset\ndataset_description = iris_df.describe()\n\n# Show the first few rows of the dataset\ndataset_head = iris_df.head()\n\n# Count the number of samples for each class\nclass_distribution = iris_df['target'].value_counts()\n\n# Output the results\ndataset_info, dataset_description, dataset_head, class_distribution"
+    "[{\"role\": \"system\", \"content\": \"You are Code Interpreter, a world-class programmer that can complete any goal by executing code. Strictly follow the plan and generate code step by step. Each step of the code will be executed on the user's machine, and the user will provide the code execution results to you.**Notice: The code for the next step depends on the code for the previous step. Must reuse variables in the lastest other code directly, dont creat it again, it is very import for you. Use !pip install in a standalone block to install missing packages.Usually the libraries you need are already installed.Dont check if packages already imported.**\"}, {\"role\": \"user\", \"content\": \"\\n## User Requirement\\nRun data analysis on sklearn Iris dataset, include a plot\\n## Context\\n\\n## Current Plan\\n[\\n    {\\n        \\\"task_id\\\": \\\"1\\\",\\n        \\\"dependent_task_ids\\\": [],\\n        \\\"instruction\\\": \\\"Load the sklearn Iris dataset.\\\",\\n        \\\"task_type\\\": \\\"other\\\",\\n        \\\"code\\\": \\\"\\\",\\n        \\\"result\\\": \\\"a successful run\\\",\\n        \\\"is_success\\\": true,\\n        \\\"is_finished\\\": true\\n    },\\n    {\\n        \\\"task_id\\\": \\\"2\\\",\\n        \\\"dependent_task_ids\\\": [\\n            \\\"1\\\"\\n        ],\\n        \\\"instruction\\\": \\\"Perform exploratory data analysis on the Iris dataset.\\\",\\n        \\\"task_type\\\": \\\"eda\\\",\\n        \\\"code\\\": \\\"\\\",\\n        \\\"result\\\": \\\"\\\",\\n        \\\"is_success\\\": false,\\n        \\\"is_finished\\\": false\\n    },\\n    {\\n        \\\"task_id\\\": \\\"3\\\",\\n        \\\"dependent_task_ids\\\": [\\n            \\\"2\\\"\\n        ],\\n        \\\"instruction\\\": \\\"Create a plot visualizing the Iris dataset.\\\",\\n        \\\"task_type\\\": \\\"other\\\",\\n        \\\"code\\\": \\\"\\\",\\n        \\\"result\\\": \\\"\\\",\\n        \\\"is_success\\\": false,\\n        \\\"is_finished\\\": false\\n    }\\n]\\n## Current Task\\n{\\\"task_id\\\":\\\"2\\\",\\\"dependent_task_ids\\\":[\\\"1\\\"],\\\"instruction\\\":\\\"Perform exploratory data analysis on the Iris dataset.\\\",\\\"task_type\\\":\\\"eda\\\",\\\"code\\\":\\\"\\\",\\\"result\\\":\\\"\\\",\\\"is_success\\\":false,\\\"is_finished\\\":false}\\n\"}, {\"role\": \"user\", \"content\": \"\\n# Instruction\\nWrite complete code for 'Current Task'. And avoid duplicating code from finished tasks, such as repeated import of packages, reading data, etc.\\nSpecifically, \\n\\n# Capabilities\\n- You can utilize pre-defined tools in any code lines from 'Available Tools' in the form of Python Class.\\n- You can freely combine the use of any other public packages, like sklearn, numpy, pandas, etc..\\n\\n# Available Tools (can be empty):\\nEach Class tool is described in JSON format. When you call a tool, import the tool first.\\n{}\\n\\n# Constraints:\\n- Ensure the output new code is executable in the same Jupyter notebook with previous tasks code have been executed.\\n- Always prioritize using pre-defined tools for the same functionality.\\n\"}]": {
+        "code": "from sklearn import datasets\nimport pandas as pd\n\n# Load Iris dataset\niris = datasets.load_iris()\ndf_iris = pd.DataFrame(data=iris.data, columns=iris.feature_names)\ndf_iris['target'] = iris.target\n\n# Display basic information about the dataset\nprint(df_iris.info())\n\n# Display statistical summary of the dataset\nprint(df_iris.describe())\n\n# Display the first few rows of the dataset\nprint(df_iris.head())\n\n# Display the distribution of the target variable\ntarget_counts = df_iris['target'].value_counts()\nprint(target_counts)"
     },
-    "[{\"role\": \"system\", \"content\": \"You are Code Interpreter, a world-class programmer that can complete any goal by executing code. Strictly follow the plan and generate code step by step. Each step of the code will be executed on the user's machine, and the user will provide the code execution results to you.**Notice: The code for the next step depends on the code for the previous step. Must reuse variables in the lastest other code directly, dont creat it again, it is very import for you. Use !pip install in a standalone block to install missing packages.Usually the libraries you need are already installed.Dont check if packages already imported.**\"}, {\"role\": \"user\", \"content\": \"\\n## User Requirement\\nRun data analysis on sklearn Iris dataset, include a plot\\n## Context\\n\\n## Current Plan\\n[\\n    {\\n        \\\"task_id\\\": \\\"1\\\",\\n        \\\"dependent_task_ids\\\": [],\\n        \\\"instruction\\\": \\\"Load the sklearn Iris dataset.\\\",\\n        \\\"task_type\\\": \\\"other\\\",\\n        \\\"code\\\": \\\"from sklearn.datasets import load_iris\\\\niris_data = load_iris()\\\",\\n        \\\"result\\\": \\\"a successful run\\\",\\n        \\\"is_success\\\": true,\\n        \\\"is_finished\\\": true\\n    },\\n    {\\n        \\\"task_id\\\": \\\"2\\\",\\n        \\\"dependent_task_ids\\\": [\\n            \\\"1\\\"\\n        ],\\n        \\\"instruction\\\": \\\"Perform exploratory data analysis on the Iris dataset.\\\",\\n        \\\"task_type\\\": \\\"eda\\\",\\n        \\\"code\\\": \\\"import pandas as pd\\\\nimport numpy as np\\\\n\\\\n# Convert the loaded Iris dataset to a DataFrame for easier manipulation\\\\niris_df = pd.DataFrame(data=np.c_[iris_data['data'], iris_data['target']], columns=iris_data['feature_names'] + ['target'])\\\\n\\\\n# Show basic information about the dataset\\\\ndataset_info = iris_df.info()\\\\n\\\\n# Show basic statistics of the dataset\\\\ndataset_description = iris_df.describe()\\\\n\\\\n# Show the first few rows of the dataset\\\\ndataset_head = iris_df.head()\\\\n\\\\n# Count the number of samples for each class\\\\nclass_distribution = iris_df['target'].value_counts()\\\\n\\\\n# Output the results\\\\ndataset_info, dataset_description, dataset_head, class_distribution\\\",\\n        \\\"result\\\": \\\"a successful run\\\",\\n        \\\"is_success\\\": true,\\n        \\\"is_finished\\\": true\\n    },\\n    {\\n        \\\"task_id\\\": \\\"3\\\",\\n        \\\"dependent_task_ids\\\": [\\n            \\\"2\\\"\\n        ],\\n        \\\"instruction\\\": \\\"Create a plot visualizing the Iris dataset.\\\",\\n        \\\"task_type\\\": \\\"other\\\",\\n        \\\"code\\\": \\\"\\\",\\n        \\\"result\\\": \\\"\\\",\\n        \\\"is_success\\\": false,\\n        \\\"is_finished\\\": false\\n    }\\n]\\n## Current Task\\n{\\\"task_id\\\":\\\"3\\\",\\\"dependent_task_ids\\\":[\\\"2\\\"],\\\"instruction\\\":\\\"Create a plot visualizing the Iris dataset.\\\",\\\"task_type\\\":\\\"other\\\",\\\"code\\\":\\\"\\\",\\\"result\\\":\\\"\\\",\\\"is_success\\\":false,\\\"is_finished\\\":false}\\n\"}, {\"role\": \"user\", \"content\": \"\\n# Instruction\\nWrite complete code for 'Current Task'. And avoid duplicating code from finished tasks, such as repeated import of packages, reading data, etc.\\nSpecifically, \\n\\n# Capabilities\\n- You can utilize pre-defined tools in any code lines from 'Available Tools' in the form of Python Class.\\n- You can freely combine the use of any other public packages, like sklearn, numpy, pandas, etc..\\n\\n# Available Tools (can be empty):\\nEach Class tool is described in JSON format. When you call a tool, import the tool first.\\n{}\\n\\n# Constraints:\\n- Ensure the output new code is executable in the same Jupyter notebook with previous tasks code have been executed.\\n- Always prioritize using pre-defined tools for the same functionality.\\n\"}]": {
-        "code": "import matplotlib.pyplot as plt\nimport seaborn as sns\n\n# Set the style of seaborn\nsns.set(style=\"whitegrid\")\n\n# Create a pairplot to visualize the relationships between features\ng = sns.pairplot(iris_df, hue='target', palette='husl', markers=['o', 's', 'D'])\n\n# Add titles and labels\ng.fig.suptitle('Iris Dataset Pairplot', y=1.02) # y=1.02 to make space for the title\nplt.show()"
+    "[{\"role\": \"system\", \"content\": \"You are Code Interpreter, a world-class programmer that can complete any goal by executing code. Strictly follow the plan and generate code step by step. Each step of the code will be executed on the user's machine, and the user will provide the code execution results to you.**Notice: The code for the next step depends on the code for the previous step. Must reuse variables in the lastest other code directly, dont creat it again, it is very import for you. Use !pip install in a standalone block to install missing packages.Usually the libraries you need are already installed.Dont check if packages already imported.**\"}, {\"role\": \"user\", \"content\": \"\\n## User Requirement\\nRun data analysis on sklearn Iris dataset, include a plot\\n## Context\\n\\n## Current Plan\\n[\\n    {\\n        \\\"task_id\\\": \\\"1\\\",\\n        \\\"dependent_task_ids\\\": [],\\n        \\\"instruction\\\": \\\"Load the sklearn Iris dataset.\\\",\\n        \\\"task_type\\\": \\\"other\\\",\\n        \\\"code\\\": \\\"\\\",\\n        \\\"result\\\": \\\"a successful run\\\",\\n        \\\"is_success\\\": true,\\n        \\\"is_finished\\\": true\\n    },\\n    {\\n        \\\"task_id\\\": \\\"2\\\",\\n        \\\"dependent_task_ids\\\": [\\n            \\\"1\\\"\\n        ],\\n        \\\"instruction\\\": \\\"Perform exploratory data analysis on the Iris dataset.\\\",\\n        \\\"task_type\\\": \\\"eda\\\",\\n        \\\"code\\\": \\\"\\\",\\n        \\\"result\\\": \\\"a successful run\\\",\\n        \\\"is_success\\\": true,\\n        \\\"is_finished\\\": true\\n    },\\n    {\\n        \\\"task_id\\\": \\\"3\\\",\\n        \\\"dependent_task_ids\\\": [\\n            \\\"2\\\"\\n        ],\\n        \\\"instruction\\\": \\\"Create a plot visualizing the Iris dataset.\\\",\\n        \\\"task_type\\\": \\\"other\\\",\\n        \\\"code\\\": \\\"\\\",\\n        \\\"result\\\": \\\"\\\",\\n        \\\"is_success\\\": false,\\n        \\\"is_finished\\\": false\\n    }\\n]\\n## Current Task\\n{\\\"task_id\\\":\\\"3\\\",\\\"dependent_task_ids\\\":[\\\"2\\\"],\\\"instruction\\\":\\\"Create a plot visualizing the Iris dataset.\\\",\\\"task_type\\\":\\\"other\\\",\\\"code\\\":\\\"\\\",\\\"result\\\":\\\"\\\",\\\"is_success\\\":false,\\\"is_finished\\\":false}\\n\"}, {\"role\": \"user\", \"content\": \"\\n# Instruction\\nWrite complete code for 'Current Task'. And avoid duplicating code from finished tasks, such as repeated import of packages, reading data, etc.\\nSpecifically, \\n\\n# Capabilities\\n- You can utilize pre-defined tools in any code lines from 'Available Tools' in the form of Python Class.\\n- You can freely combine the use of any other public packages, like sklearn, numpy, pandas, etc..\\n\\n# Available Tools (can be empty):\\nEach Class tool is described in JSON format. When you call a tool, import the tool first.\\n{}\\n\\n# Constraints:\\n- Ensure the output new code is executable in the same Jupyter notebook with previous tasks code have been executed.\\n- Always prioritize using pre-defined tools for the same functionality.\\n\"}]": {
+        "code": "from sklearn import datasets\nimport matplotlib.pyplot as plt\nimport pandas as pd\n\n# Load Iris dataset\niris = datasets.load_iris()\niris_df = pd.DataFrame(data=iris.data, columns=iris.feature_names)\niris_df['target'] = iris.target\niris_df['target_name'] = iris_df['target'].apply(lambda x: iris.target_names[x])\n\n# Plotting\nfig, ax = plt.subplots(figsize=(12, 8))\nfor target, target_name in zip(iris.target_names, iris.target_names):\n    subset = iris_df[iris_df['target_name'] == target_name]\n    ax.scatter(subset[iris.feature_names[0]], subset[iris.feature_names[1]], label=target_name)\n\nax.set_xlabel(iris.feature_names[0])\nax.set_ylabel(iris.feature_names[1])\nax.legend()\nplt.show()"
     },
     "[{\"role\": \"user\", \"content\": \"\\n# Background\\nAs a data scientist, you need to help user to achieve their goal [This is a titanic passenger survival dataset, your goal is to predict passenger survival outcome. The target column is Survived. Perform data analysis, data preprocessing, feature engineering, and modeling to predict the target. Report accuracy on the eval data. Train data path: 'tests/data/ml_datasets/titanic/split_train.csv', eval data path: 'tests/data/ml_datasets/titanic/split_eval.csv'.] step-by-step in an continuous Jupyter notebook.\\n\\n## Done Tasks\\n```python\\n\\n```end\\n\\n## Current Task\\nPerform exploratory data analysis on the train dataset to understand the features and target variable.\\n\\n# Latest Data Info\\nLatest data info after previous tasks:\\n\\n\\n# Task\\nWrite complete code for 'Current Task'. And avoid duplicating code from 'Done Tasks', such as repeated import of packages, reading data, etc.\\nSpecifically, \\n\\n# Output Example:\\nwhen current task is \\\"train a lightgbm model on training data\\\", the code can be like:\\n```python\\n# Step 1: check data type and convert to numeric\\nobj_cols = train.select_dtypes(include='object').columns.tolist()\\n\\nfor col in obj_cols:\\n    encoder = LabelEncoder()\\n    train[col] = encoder.fit_transform(train[col].unique().tolist() + ['unknown'])\\n    test[col] = test[col].apply(lambda x: x if x in encoder.classes_ else 'unknown')\\n    test[col] = encoder.transform(test[col])\\n\\n# Step 2: train lightgbm model\\nmodel = LGBMClassifier()\\nmodel.fit(train, y_train)\\n```end\\n\\n# Constraints:\\n- Ensure the output new code is executable in the same Jupyter notebook with previous tasks code have been executed.\\n\"}]": {
         "code": "# Perform exploratory data analysis on the train dataset\ndf_train = pd.read_csv('tests/data/ml_datasets/titanic/split_train.csv')\n\n# Display the first few rows of the dataset\ndisplay(df_train.head())\n\n# Summary statistics for numerical features\ndisplay(df_train.describe())\n\n# Summary information about the dataset including the data types and number of non-null values\ndisplay(df_train.info())\n\n# Distribution of the target variable 'Survived'\nsurvival_counts = df_train['Survived'].value_counts()\nprint(\"Survival counts:\\n\", survival_counts)\n\n# Visualizations\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\n# Distribution of the target variable\nsns.countplot(x='Survived', data=df_train)\nplt.title('Distribution of Survival')\nplt.show()\n\n# Correlation matrix heatmap to understand the relationship between features\nplt.figure(figsize=(10, 8))\nsns.heatmap(df_train.corr(), annot=True, fmt='.2f')\nplt.title('Correlation Matrix')\nplt.show()\n\n# Pairplot to visualize the pairwise relationships between features\nsns.pairplot(df_train, hue='Survived')\nplt.title('Pairplot of Features')\nplt.show()"
@@ -389,11 +389,5 @@
         "reflection": "The implementation failed the test case where the input array is [1, 5, 2, 3, 4]. The issue arises because the code does not handle the sorting of elements with the same number of ones in their binary representation correctly. According to the context, for similar number of ones, the elements should be sorted based on their decimal value in ascending order. However, the output [1, 2, 4, 3, 5] shows that 3 and 4 are not in the correct order, indicating that the sorting by decimal value is not functioning as intended. To fix this issue, we should ensure that the sorting is stable, so that when two elements have the same number of ones, they maintain their relative order if their decimal values are already in ascending order.",
         "improved_impl": "def sort_array(arr):\n    # Helper function to count the number of ones in the binary representation\n    def count_ones(n):\n        return bin(n).count('1')\n    \n    # Sort the array using a custom key function\n    # The key function returns a tuple (number of ones, value) for each element\n    # This ensures that if two elements have the same number of ones, they are sorted by their value\n    # We use a stable sorting algorithm to maintain the relative order of elements with the same number of ones\n    sorted_arr = sorted(arr, key=lambda x: (count_ones(x), x), reverse=True)\n    sorted_arr = sorted(sorted_arr, key=count_ones)\n    \n    return sorted_arr\n"
     },
-    "[{\"role\": \"system\", \"content\": \"You are Code Interpreter, a world-class programmer that can complete any goal by executing code. Strictly follow the plan and generate code step by step. Each step of the code will be executed on the user's machine, and the user will provide the code execution results to you.**Notice: The code for the next step depends on the code for the previous step. Must reuse variables in the lastest other code directly, dont creat it again, it is very import for you. Use !pip install in a standalone block to install missing packages.Usually the libraries you need are already installed.Dont check if packages already imported.**\"}, {\"role\": \"user\", \"content\": \"\\n## User Requirement\\nRun data analysis on sklearn Iris dataset, include a plot\\n## Context\\n\\n## Current Plan\\n[\\n    {\\n        \\\"task_id\\\": \\\"1\\\",\\n        \\\"dependent_task_ids\\\": [],\\n        \\\"instruction\\\": \\\"Load the sklearn Iris dataset.\\\",\\n        \\\"task_type\\\": \\\"other\\\",\\n        \\\"code\\\": \\\"\\\",\\n        \\\"result\\\": \\\"a successful run\\\",\\n        \\\"is_success\\\": true,\\n        \\\"is_finished\\\": true\\n    },\\n    {\\n        \\\"task_id\\\": \\\"2\\\",\\n        \\\"dependent_task_ids\\\": [\\n            \\\"1\\\"\\n        ],\\n        \\\"instruction\\\": \\\"Perform exploratory data analysis on the Iris dataset.\\\",\\n        \\\"task_type\\\": \\\"eda\\\",\\n        \\\"code\\\": \\\"\\\",\\n        \\\"result\\\": \\\"\\\",\\n        \\\"is_success\\\": false,\\n        \\\"is_finished\\\": false\\n    },\\n    {\\n        \\\"task_id\\\": \\\"3\\\",\\n        \\\"dependent_task_ids\\\": [\\n            \\\"2\\\"\\n        ],\\n        \\\"instruction\\\": \\\"Create a plot visualizing the Iris dataset.\\\",\\n        \\\"task_type\\\": \\\"other\\\",\\n        \\\"code\\\": \\\"\\\",\\n        \\\"result\\\": \\\"\\\",\\n        \\\"is_success\\\": false,\\n        \\\"is_finished\\\": false\\n    }\\n]\\n## Current Task\\n{\\\"task_id\\\":\\\"2\\\",\\\"dependent_task_ids\\\":[\\\"1\\\"],\\\"instruction\\\":\\\"Perform exploratory data analysis on the Iris dataset.\\\",\\\"task_type\\\":\\\"eda\\\",\\\"code\\\":\\\"\\\",\\\"result\\\":\\\"\\\",\\\"is_success\\\":false,\\\"is_finished\\\":false}\\n\"}, {\"role\": \"user\", \"content\": \"\\n# Instruction\\nWrite complete code for 'Current Task'. And avoid duplicating code from finished tasks, such as repeated import of packages, reading data, etc.\\nSpecifically, \\n\\n# Capabilities\\n- You can utilize pre-defined tools in any code lines from 'Available Tools' in the form of Python Class.\\n- You can freely combine the use of any other public packages, like sklearn, numpy, pandas, etc..\\n\\n# Available Tools (can be empty):\\nEach Class tool is described in JSON format. When you call a tool, import the tool first.\\n{}\\n\\n# Constraints:\\n- Ensure the output new code is executable in the same Jupyter notebook with previous tasks code have been executed.\\n- Always prioritize using pre-defined tools for the same functionality.\\n\"}]": {
-        "code": "from sklearn import datasets\nimport pandas as pd\n\n# Load Iris dataset\niris = datasets.load_iris()\ndf_iris = pd.DataFrame(data=iris.data, columns=iris.feature_names)\ndf_iris['target'] = iris.target\n\n# Display basic information about the dataset\nprint(df_iris.info())\n\n# Display statistical summary of the dataset\nprint(df_iris.describe())\n\n# Display the first few rows of the dataset\nprint(df_iris.head())\n\n# Display the distribution of the target variable\ntarget_counts = df_iris['target'].value_counts()\nprint(target_counts)"
-    },
-    "[{\"role\": \"system\", \"content\": \"You are Code Interpreter, a world-class programmer that can complete any goal by executing code. Strictly follow the plan and generate code step by step. Each step of the code will be executed on the user's machine, and the user will provide the code execution results to you.**Notice: The code for the next step depends on the code for the previous step. Must reuse variables in the lastest other code directly, dont creat it again, it is very import for you. Use !pip install in a standalone block to install missing packages.Usually the libraries you need are already installed.Dont check if packages already imported.**\"}, {\"role\": \"user\", \"content\": \"\\n## User Requirement\\nRun data analysis on sklearn Iris dataset, include a plot\\n## Context\\n\\n## Current Plan\\n[\\n    {\\n        \\\"task_id\\\": \\\"1\\\",\\n        \\\"dependent_task_ids\\\": [],\\n        \\\"instruction\\\": \\\"Load the sklearn Iris dataset.\\\",\\n        \\\"task_type\\\": \\\"other\\\",\\n        \\\"code\\\": \\\"\\\",\\n        \\\"result\\\": \\\"a successful run\\\",\\n        \\\"is_success\\\": true,\\n        \\\"is_finished\\\": true\\n    },\\n    {\\n        \\\"task_id\\\": \\\"2\\\",\\n        \\\"dependent_task_ids\\\": [\\n            \\\"1\\\"\\n        ],\\n        \\\"instruction\\\": \\\"Perform exploratory data analysis on the Iris dataset.\\\",\\n        \\\"task_type\\\": \\\"eda\\\",\\n        \\\"code\\\": \\\"\\\",\\n        \\\"result\\\": \\\"a successful run\\\",\\n        \\\"is_success\\\": true,\\n        \\\"is_finished\\\": true\\n    },\\n    {\\n        \\\"task_id\\\": \\\"3\\\",\\n        \\\"dependent_task_ids\\\": [\\n            \\\"2\\\"\\n        ],\\n        \\\"instruction\\\": \\\"Create a plot visualizing the Iris dataset.\\\",\\n        \\\"task_type\\\": \\\"other\\\",\\n        \\\"code\\\": \\\"\\\",\\n        \\\"result\\\": \\\"\\\",\\n        \\\"is_success\\\": false,\\n        \\\"is_finished\\\": false\\n    }\\n]\\n## Current Task\\n{\\\"task_id\\\":\\\"3\\\",\\\"dependent_task_ids\\\":[\\\"2\\\"],\\\"instruction\\\":\\\"Create a plot visualizing the Iris dataset.\\\",\\\"task_type\\\":\\\"other\\\",\\\"code\\\":\\\"\\\",\\\"result\\\":\\\"\\\",\\\"is_success\\\":false,\\\"is_finished\\\":false}\\n\"}, {\"role\": \"user\", \"content\": \"\\n# Instruction\\nWrite complete code for 'Current Task'. And avoid duplicating code from finished tasks, such as repeated import of packages, reading data, etc.\\nSpecifically, \\n\\n# Capabilities\\n- You can utilize pre-defined tools in any code lines from 'Available Tools' in the form of Python Class.\\n- You can freely combine the use of any other public packages, like sklearn, numpy, pandas, etc..\\n\\n# Available Tools (can be empty):\\nEach Class tool is described in JSON format. When you call a tool, import the tool first.\\n{}\\n\\n# Constraints:\\n- Ensure the output new code is executable in the same Jupyter notebook with previous tasks code have been executed.\\n- Always prioritize using pre-defined tools for the same functionality.\\n\"}]": {
-        "code": "from sklearn import datasets\nimport matplotlib.pyplot as plt\nimport pandas as pd\n\n# Load Iris dataset\niris = datasets.load_iris()\niris_df = pd.DataFrame(data=iris.data, columns=iris.feature_names)\niris_df['target'] = iris.target\niris_df['target_name'] = iris_df['target'].apply(lambda x: iris.target_names[x])\n\n# Plotting\nfig, ax = plt.subplots(figsize=(12, 8))\nfor target, target_name in zip(iris.target_names, iris.target_names):\n    subset = iris_df[iris_df['target_name'] == target_name]\n    ax.scatter(subset[iris.feature_names[0]], subset[iris.feature_names[1]], label=target_name)\n\nax.set_xlabel(iris.feature_names[0])\nax.set_ylabel(iris.feature_names[1])\nax.legend()\nplt.show()"
-    },
     "\n## context\n\n\n-----\n\n## format example\n[CONTENT]\n{\n    \"invoice\": \"False\"\n}\n[/CONTENT]\n\n## nodes: \"<node>: <type>  # <instruction>\"\n- invoice: <class 'bool'>  # if it's a invoice file, return True else False\n\n\n## constraint\nLanguage: Please use the same language as Human INPUT.\nFormat: output wrapped inside [CONTENT][/CONTENT] like format example, nothing else.\n\n## action\nFollow instructions of nodes, generate output and make sure it follows the format example.\n": "[CONTENT]\n{\n    \"invoice\": \"True\"\n}\n[/CONTENT]"
 }
\ No newline at end of file
diff --git a/tests/metagpt/tools/test_tool_convert.py b/tests/metagpt/tools/test_tool_convert.py
index 2ae2ea000..8f26a211c 100644
--- a/tests/metagpt/tools/test_tool_convert.py
+++ b/tests/metagpt/tools/test_tool_convert.py
@@ -95,12 +95,26 @@ def dummy_fn(df: pd.DataFrame) -> dict:
     pass
 
 
+async def dummy_async_fn(df: pd.DataFrame) -> dict:
+    """
+    A dummy async function for test
+
+    Args:
+        df (pd.DataFrame): test args.
+
+    Returns:
+        dict: test returns.
+    """
+    pass
+
+
 def test_convert_code_to_tool_schema_class():
     expected = {
         "type": "class",
         "description": "Completing missing values with simple strategies.",
         "methods": {
             "__init__": {
+                "type": "function",
                 "description": "Initialize self.",
                 "parameters": {
                     "properties": {
@@ -121,6 +135,7 @@ def test_convert_code_to_tool_schema_class():
                 },
             },
             "fit": {
+                "type": "function",
                 "description": "Fit the FillMissingValue model.",
                 "parameters": {
                     "properties": {"df": {"type": "pd.DataFrame", "description": "The input DataFrame."}},
@@ -128,6 +143,7 @@ def test_convert_code_to_tool_schema_class():
                 },
             },
             "transform": {
+                "type": "function",
                 "description": "Transform the input DataFrame with the fitted model.",
                 "parameters": {
                     "properties": {"df": {"type": "pd.DataFrame", "description": "The input DataFrame."}},
@@ -152,3 +168,8 @@ def test_convert_code_to_tool_schema_function():
     }
     schema = convert_code_to_tool_schema(dummy_fn)
     assert schema == expected
+
+
+def test_convert_code_to_tool_schema_async_function():
+    schema = convert_code_to_tool_schema(dummy_async_fn)
+    assert schema.get("type") == "async_function"